Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification.
ABSTRACT There is growing awareness that the results of randomized clinical trials might not apply in a straightforward way to individual patients, even those within the trial. Although randomization theoretically ensures the comparability of treatment groups overall, there remain important differences between individuals in each treatment group that can dramatically affect the likelihood of benefiting from or being harmed by a therapy.1- 4 Averaging effects across such different patients can give misleading results to physicians who care for individual, not average, patients.
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ABSTRACT: To determine whether some participants in the Diabetes Prevention Program were more or less likely to benefit from metformin or a structured lifestyle modification program. Post hoc analysis of the Diabetes Prevention Program, a randomized controlled trial. Ambulatory care patients. 3060 people without diabetes but with evidence of impaired glucose metabolism. Intervention groups received metformin or a lifestyle modification program with the goals of weight loss and physical activity. Development of diabetes, stratified by the risk of developing diabetes according to a diabetes risk prediction model. Of the 3081 participants with impaired glucose metabolism at baseline, 655 (21%) progressed to diabetes over a median 2.8 years' follow-up. The diabetes risk model had good discrimination (C statistic=0.73) and calibration. Although the lifestyle intervention provided a sixfold greater absolute risk reduction in the highest risk quarter than in the lowest risk quarter, patients in the lowest risk quarter still received substantial benefit (three year absolute risk reduction 4.9% v 28.3% in highest risk quarter; numbers needed to treat of 20.4 and 3.5, respectively). The benefit of metformin, however, was seen almost entirely in patients in the top quarter of risk of diabetes. No benefit was seen in the lowest risk quarter. Participants in the highest risk quarter averaged a 21.4% three year absolute risk reduction (number needed to treat 4.6). Patients at high risk of diabetes have substantial variation in their likelihood of receiving benefit from diabetes prevention treatments. Using this knowledge could decrease overtreatment and make prevention of diabetes far more efficient, effective, and patient centered, provided that decision making is based on an accurate risk prediction tool. © Sussman et al 2015.BMJ Clinical Research 02/2015; 350:h454. DOI:10.1136/bmj.h454 · 14.09 Impact Factor
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ABSTRACT: Four species of lactic acid bacteria (LAB) were isolated from homemade yoghurt samples and defined as Lactobacillus acidophilus Lactobacillus bulgaricus, Lactobacillus casei, and Lactobacillus plantarum. Each LAB isolate was tested for its tolerance to acidic environments at pH (7.0, 4.0, and 2.0). All lactobacilli isolated tolerated acid while L. bulgaricus was sensitive to pH 2.0. All four bacteria were resistant to ciprofloxacin. Isolates were further tested for their antimicrobial activity against common pathogenic bacteria such as Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, Staphylococcus aureus (MRSA), and Bacillus subtilis using the agar spot method. These lactic acid bacteria were found to inhibit growth of most pathogens tested. The viability of lactobacilli isolates were not affected by storage (for 6 weeks) at -20PoPC and 4PoPC but declined when they were stored at room temperature. Acid tolerance and bacterial antagonistic characteristics of these LAB isolates render them good candidates for consideration as probiotics.09/2013; 6(3):211-216. DOI:10.12816/0001535
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ABSTRACT: In randomized controlled trials (RCTs), the most compelling need is to determine whether the treatment condition was more effective than control. However, it is generally recognized that not all participants in the treatment group of most clinical trials benefit equally. While subgroup analyses are often used to compare treatment effectiveness across pre-determined subgroups categorized by patient characteristics, methods to empirically identify naturally occurring clusters of persons who benefit most from the treatment group have rarely been implemented. This article provides a modeling framework to accomplish this important task. Utilizing information about individuals from the treatment group who had poor outcomes, the present study proposes an a priori clustering strategy that classifies the individuals with initially good outcomes in the treatment group into: (a) group GE (good outcome, effective), the latent subgroup of individuals for whom the treatment is likely to be effective and (b) group GI (good outcome, ineffective), the latent subgroup of individuals for whom the treatment is not likely to be effective. The method is illustrated through a re-analysis of a publically available data set from the National Institute on Drug Abuse. The RCT examines the effectiveness of motivational enhancement therapy from 461 outpatients with substance abuse problems. The proposed method identified latent subgroups GE and GI, and the comparison between the two groups revealed several significantly different and informative characteristics even though both subgroups had good outcomes during the immediate post-therapy period. As a diagnostic means utilizing out-of-sample forecasting performance, the present study compared the relapse rates during the long-term follow-up period for the two subgroups. As expected, group GI, composed of individuals for whom the treatment was hypothesized to be ineffective, had a significantly higher relapse rate than group GE (63% vs. 27%; χ (2) = 9.99, p-value = .002).